Post authored by Lora Leligdon

Data has to be readable and well-documented enough for others (and a future you) to understand.

Data has to be findable to keep it from being lost. Information scientists have started to call such data FAIR — Findable, Accessible, Interoperable, Re-usable. One of the most important things you can do to keep your data FAIR is to deposit it in a trusted digital repository. Do not use your personal website as your data archive.

Tidy data are good data. Messy data are hard to work with.

Data quality is a process, starting with planning through to curation of the data for deposit.

Stories

Example: This dataset is still around and usable more than 50 years after the data were collected and more than 40 years after it was last used in a publication.

Want to learn more? Register and attend a Dartmouth research data management workshop to learn more about planning, cleaning, visualizing, storing, sharing, and preserving your data at Dartmouth.

What is your favorite data set? How/why is it good for your project? Try out the FAIR Principles to describe and share examples of good data for your discipline. Tell us on Twitter or Facebook (#LYD 2017 #loveyourdata)